Potential impact of learning management zones for site-specific N fertilisation: A case study for wheat crops

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Potential impact of learning management zones for site-specific N fertilisation : A case study for wheat crops. / Franco, Camilo; Mejía, Nicolás; Pedersen, Søren Marcus; Gislum, René.

In: Nitrogen, Vol. 3, No. 2, 2022, p. 387-403.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Franco, C, Mejía, N, Pedersen, SM & Gislum, R 2022, 'Potential impact of learning management zones for site-specific N fertilisation: A case study for wheat crops', Nitrogen, vol. 3, no. 2, pp. 387-403. https://doi.org/10.3390/nitrogen3020025

APA

Franco, C., Mejía, N., Pedersen, S. M., & Gislum, R. (2022). Potential impact of learning management zones for site-specific N fertilisation: A case study for wheat crops. Nitrogen, 3(2), 387-403. https://doi.org/10.3390/nitrogen3020025

Vancouver

Franco C, Mejía N, Pedersen SM, Gislum R. Potential impact of learning management zones for site-specific N fertilisation: A case study for wheat crops. Nitrogen. 2022;3(2):387-403. https://doi.org/10.3390/nitrogen3020025

Author

Franco, Camilo ; Mejía, Nicolás ; Pedersen, Søren Marcus ; Gislum, René. / Potential impact of learning management zones for site-specific N fertilisation : A case study for wheat crops. In: Nitrogen. 2022 ; Vol. 3, No. 2. pp. 387-403.

Bibtex

@article{45160db962894077a74eb2a9c40886f4,
title = "Potential impact of learning management zones for site-specific N fertilisation: A case study for wheat crops",
abstract = "This paper proposes an automatic, machine learning methodology for precision agriculture, aiming at learning management zones that allow a more efficient and sustainable use of fertiliser. In particular, the methodology consists of clustering remote sensing data and estimating the impact of decision-making based on the extracted knowledge. A case study is developed on experimental data coming from winter wheat (Triticum aestivum) crops receiving site-specific fertilisation. A first approximation to the data allows measuring the effects of the fertilisation treatments on the yield and quality of the crops. After verifying the significance of such effects, clustering analysis is applied on sensor readings on vegetation and soil electric conductivity in order to automatically learn the best configuration of zones for differentiated treatment. The complete methodology for identifying management zones from vegetation and soil sensing is validated for two experimental sites in Denmark, estimating its potential impact for decision-making on site-specific N fertilisation.",
author = "Camilo Franco and Nicol{\'a}s Mej{\'i}a and Pedersen, {S{\o}ren Marcus} and Ren{\'e} Gislum",
year = "2022",
doi = "10.3390/nitrogen3020025",
language = "English",
volume = "3",
pages = "387--403",
journal = "Nitrogen",
issn = "2504-3129",
publisher = "MDPI",
number = "2",

}

RIS

TY - JOUR

T1 - Potential impact of learning management zones for site-specific N fertilisation

T2 - A case study for wheat crops

AU - Franco, Camilo

AU - Mejía, Nicolás

AU - Pedersen, Søren Marcus

AU - Gislum, René

PY - 2022

Y1 - 2022

N2 - This paper proposes an automatic, machine learning methodology for precision agriculture, aiming at learning management zones that allow a more efficient and sustainable use of fertiliser. In particular, the methodology consists of clustering remote sensing data and estimating the impact of decision-making based on the extracted knowledge. A case study is developed on experimental data coming from winter wheat (Triticum aestivum) crops receiving site-specific fertilisation. A first approximation to the data allows measuring the effects of the fertilisation treatments on the yield and quality of the crops. After verifying the significance of such effects, clustering analysis is applied on sensor readings on vegetation and soil electric conductivity in order to automatically learn the best configuration of zones for differentiated treatment. The complete methodology for identifying management zones from vegetation and soil sensing is validated for two experimental sites in Denmark, estimating its potential impact for decision-making on site-specific N fertilisation.

AB - This paper proposes an automatic, machine learning methodology for precision agriculture, aiming at learning management zones that allow a more efficient and sustainable use of fertiliser. In particular, the methodology consists of clustering remote sensing data and estimating the impact of decision-making based on the extracted knowledge. A case study is developed on experimental data coming from winter wheat (Triticum aestivum) crops receiving site-specific fertilisation. A first approximation to the data allows measuring the effects of the fertilisation treatments on the yield and quality of the crops. After verifying the significance of such effects, clustering analysis is applied on sensor readings on vegetation and soil electric conductivity in order to automatically learn the best configuration of zones for differentiated treatment. The complete methodology for identifying management zones from vegetation and soil sensing is validated for two experimental sites in Denmark, estimating its potential impact for decision-making on site-specific N fertilisation.

U2 - 10.3390/nitrogen3020025

DO - 10.3390/nitrogen3020025

M3 - Journal article

VL - 3

SP - 387

EP - 403

JO - Nitrogen

JF - Nitrogen

SN - 2504-3129

IS - 2

ER -

ID: 318707030